{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a71851ed-d72d-4978-a5db-b2a7f66bbbcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching packages\u001b[22m ────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.2 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.4.0      \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 1.0.0 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.8      \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.10\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.2.1      \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.5.0 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.3      \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.2 \n",
      "── \u001b[1mConflicts\u001b[22m ───────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "library(cowplot)\n",
    "library(ggsci)\n",
    "library(RColorBrewer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a584c2-ba2b-4243-9053-20b2949f474a",
   "metadata": {},
   "source": [
    "# Microbiome figure: Taxa barcharts for baby 108"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5885221c-e56f-4e2c-a0e3-887223e43723",
   "metadata": {},
   "source": [
    "## Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2c44fa70-baae-4432-9ded-8f63db16ef8d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m709\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m26\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m  (11): SampleID, SubmissionType, DiversigenCheckInSampleName, BoxLocatio...\n",
      "\u001b[32mdbl\u001b[39m   (7): SampleNumber, BabyN, Plate, Row, Column, age_at_collection, Count\n",
      "\u001b[33mlgl\u001b[39m   (6): SampleIDValidation, BabyN_checked, DOB_checked, CollectionDate_ch...\n",
      "\u001b[34mdate\u001b[39m  (2): DOB, CollectionDate\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m709\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m6\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): SampleID\n",
      "\u001b[32mdbl\u001b[39m (2): days_since_abx_start, days_since_abx_end\n",
      "\u001b[33mlgl\u001b[39m (3): never_abx, on_abx, previous_abx\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m594\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m57\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m  (3): SampleID, VR_group, VR_group_v2\n",
      "\u001b[32mdbl\u001b[39m (48): PT, Dip, FHA, PRN, TET, PRP (Hib), PCV ST1, PCV ST3, PCV ST4, PCV ...\n",
      "\u001b[33mlgl\u001b[39m  (6): PT_protected, Dip_protected, FHA_protected, PRN_protected, TET_pro...\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m736\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m7\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): SampleID\n",
      "\u001b[32mdbl\u001b[39m (6): genus_shannon, genus_evenness, genus_richness, species_shannon, spe...\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m736\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m4\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): SampleID\n",
      "\u001b[32mdbl\u001b[39m (3): ko_shannon, ko_evenness, ko_richness\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m709\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m4\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): SampleID\n",
      "\u001b[32mdbl\u001b[39m (3): MDS1, MDS2, MDS3\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m636\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m4\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): SampleID\n",
      "\u001b[32mdbl\u001b[39m (3): MDS1, MDS2, MDS3\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 102</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>SampleID</th><th scope=col>SubmissionType</th><th scope=col>SampleNumber</th><th scope=col>SampleIDValidation</th><th scope=col>DiversigenCheckInSampleName</th><th scope=col>BoxLocation</th><th scope=col>SampleType</th><th scope=col>SampleSource</th><th scope=col>SequencingType</th><th scope=col>BabyN</th><th scope=col>⋯</th><th scope=col>species_richness</th><th scope=col>ko_shannon</th><th scope=col>ko_evenness</th><th scope=col>ko_richness</th><th scope=col>Kraken_MDS1</th><th scope=col>Kraken_MDS2</th><th scope=col>Kraken_MDS3</th><th scope=col>KO_MDS1</th><th scope=col>KO_MDS2</th><th scope=col>KO_MDS3</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>204_V5</td><td>Primary in Tube</td><td>1</td><td>NA</td><td>204_S</td><td>Box 7, A1</td><td>Stool</td><td>Human Infant</td><td>MetaG</td><td>204</td><td>⋯</td><td>262</td><td> 9.631071</td><td>0.06754679</td><td>3552</td><td> 0.04696486</td><td>-0.6813761</td><td>-0.1710681</td><td> 0.361944587</td><td>-0.2445720</td><td> 0.13084552</td></tr>\n",
       "\t<tr><td>226_V1</td><td>Primary in Tube</td><td>2</td><td>NA</td><td>NA   </td><td>Box 7, A2</td><td>Stool</td><td>Human Infant</td><td>MetaG</td><td>226</td><td>⋯</td><td>143</td><td>11.065302</td><td>0.45952748</td><td>3481</td><td>-1.68264332</td><td>-0.2294583</td><td>-0.1636196</td><td>-0.808974891</td><td>-0.1701531</td><td> 0.05333913</td></tr>\n",
       "\t<tr><td>107_V3</td><td>Primary in Tube</td><td>3</td><td>NA</td><td>NA   </td><td>Box 7, A3</td><td>Stool</td><td>Human Infant</td><td>MetaG</td><td>107</td><td>⋯</td><td>267</td><td>10.381667</td><td>0.20224759</td><td>3664</td><td>-0.27366582</td><td>-0.8886653</td><td>-0.3349565</td><td> 0.004286036</td><td>-0.2128138</td><td>-0.38590665</td></tr>\n",
       "\t<tr><td>108_V3</td><td>Primary in Tube</td><td>4</td><td>NA</td><td>NA   </td><td>Box 7, A4</td><td>Stool</td><td>Human Infant</td><td>MetaG</td><td>108</td><td>⋯</td><td>581</td><td>10.545280</td><td>0.26200950</td><td>3485</td><td>-0.33309773</td><td> 0.7469011</td><td> 0.4898332</td><td>-0.054777557</td><td> 0.5141869</td><td>-0.06048777</td></tr>\n",
       "\t<tr><td>109_V1</td><td>Primary in Tube</td><td>5</td><td>NA</td><td>NA   </td><td>Box 7, A5</td><td>Stool</td><td>Human Infant</td><td>MetaG</td><td>109</td><td>⋯</td><td>193</td><td> 9.624191</td><td>0.11626061</td><td>2458</td><td>-0.80339905</td><td>-0.6660686</td><td>-0.5369415</td><td> 0.367008649</td><td>-0.2352781</td><td> 0.17121560</td></tr>\n",
       "\t<tr><td>108_V2</td><td>Primary in Tube</td><td>6</td><td>NA</td><td>NA   </td><td>Box 7, A6</td><td>Stool</td><td>Human Infant</td><td>MetaG</td><td>108</td><td>⋯</td><td>178</td><td> 9.220455</td><td>0.10696066</td><td>2822</td><td>-0.99773114</td><td> 1.0968788</td><td> 0.3873633</td><td> 0.193437912</td><td> 0.7153323</td><td> 0.55921270</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 102\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " SampleID & SubmissionType & SampleNumber & SampleIDValidation & DiversigenCheckInSampleName & BoxLocation & SampleType & SampleSource & SequencingType & BabyN & ⋯ & species\\_richness & ko\\_shannon & ko\\_evenness & ko\\_richness & Kraken\\_MDS1 & Kraken\\_MDS2 & Kraken\\_MDS3 & KO\\_MDS1 & KO\\_MDS2 & KO\\_MDS3\\\\\n",
       " <chr> & <chr> & <dbl> & <lgl> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 204\\_V5 & Primary in Tube & 1 & NA & 204\\_S & Box 7, A1 & Stool & Human Infant & MetaG & 204 & ⋯ & 262 &  9.631071 & 0.06754679 & 3552 &  0.04696486 & -0.6813761 & -0.1710681 &  0.361944587 & -0.2445720 &  0.13084552\\\\\n",
       "\t 226\\_V1 & Primary in Tube & 2 & NA & NA    & Box 7, A2 & Stool & Human Infant & MetaG & 226 & ⋯ & 143 & 11.065302 & 0.45952748 & 3481 & -1.68264332 & -0.2294583 & -0.1636196 & -0.808974891 & -0.1701531 &  0.05333913\\\\\n",
       "\t 107\\_V3 & Primary in Tube & 3 & NA & NA    & Box 7, A3 & Stool & Human Infant & MetaG & 107 & ⋯ & 267 & 10.381667 & 0.20224759 & 3664 & -0.27366582 & -0.8886653 & -0.3349565 &  0.004286036 & -0.2128138 & -0.38590665\\\\\n",
       "\t 108\\_V3 & Primary in Tube & 4 & NA & NA    & Box 7, A4 & Stool & Human Infant & MetaG & 108 & ⋯ & 581 & 10.545280 & 0.26200950 & 3485 & -0.33309773 &  0.7469011 &  0.4898332 & -0.054777557 &  0.5141869 & -0.06048777\\\\\n",
       "\t 109\\_V1 & Primary in Tube & 5 & NA & NA    & Box 7, A5 & Stool & Human Infant & MetaG & 109 & ⋯ & 193 &  9.624191 & 0.11626061 & 2458 & -0.80339905 & -0.6660686 & -0.5369415 &  0.367008649 & -0.2352781 &  0.17121560\\\\\n",
       "\t 108\\_V2 & Primary in Tube & 6 & NA & NA    & Box 7, A6 & Stool & Human Infant & MetaG & 108 & ⋯ & 178 &  9.220455 & 0.10696066 & 2822 & -0.99773114 &  1.0968788 &  0.3873633 &  0.193437912 &  0.7153323 &  0.55921270\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 102\n",
       "\n",
       "| SampleID &lt;chr&gt; | SubmissionType &lt;chr&gt; | SampleNumber &lt;dbl&gt; | SampleIDValidation &lt;lgl&gt; | DiversigenCheckInSampleName &lt;chr&gt; | BoxLocation &lt;chr&gt; | SampleType &lt;chr&gt; | SampleSource &lt;chr&gt; | SequencingType &lt;chr&gt; | BabyN &lt;dbl&gt; | ⋯ ⋯ | species_richness &lt;dbl&gt; | ko_shannon &lt;dbl&gt; | ko_evenness &lt;dbl&gt; | ko_richness &lt;dbl&gt; | Kraken_MDS1 &lt;dbl&gt; | Kraken_MDS2 &lt;dbl&gt; | Kraken_MDS3 &lt;dbl&gt; | KO_MDS1 &lt;dbl&gt; | KO_MDS2 &lt;dbl&gt; | KO_MDS3 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 204_V5 | Primary in Tube | 1 | NA | 204_S | Box 7, A1 | Stool | Human Infant | MetaG | 204 | ⋯ | 262 |  9.631071 | 0.06754679 | 3552 |  0.04696486 | -0.6813761 | -0.1710681 |  0.361944587 | -0.2445720 |  0.13084552 |\n",
       "| 226_V1 | Primary in Tube | 2 | NA | NA    | Box 7, A2 | Stool | Human Infant | MetaG | 226 | ⋯ | 143 | 11.065302 | 0.45952748 | 3481 | -1.68264332 | -0.2294583 | -0.1636196 | -0.808974891 | -0.1701531 |  0.05333913 |\n",
       "| 107_V3 | Primary in Tube | 3 | NA | NA    | Box 7, A3 | Stool | Human Infant | MetaG | 107 | ⋯ | 267 | 10.381667 | 0.20224759 | 3664 | -0.27366582 | -0.8886653 | -0.3349565 |  0.004286036 | -0.2128138 | -0.38590665 |\n",
       "| 108_V3 | Primary in Tube | 4 | NA | NA    | Box 7, A4 | Stool | Human Infant | MetaG | 108 | ⋯ | 581 | 10.545280 | 0.26200950 | 3485 | -0.33309773 |  0.7469011 |  0.4898332 | -0.054777557 |  0.5141869 | -0.06048777 |\n",
       "| 109_V1 | Primary in Tube | 5 | NA | NA    | Box 7, A5 | Stool | Human Infant | MetaG | 109 | ⋯ | 193 |  9.624191 | 0.11626061 | 2458 | -0.80339905 | -0.6660686 | -0.5369415 |  0.367008649 | -0.2352781 |  0.17121560 |\n",
       "| 108_V2 | Primary in Tube | 6 | NA | NA    | Box 7, A6 | Stool | Human Infant | MetaG | 108 | ⋯ | 178 |  9.220455 | 0.10696066 | 2822 | -0.99773114 |  1.0968788 |  0.3873633 |  0.193437912 |  0.7153323 |  0.55921270 |\n",
       "\n"
      ],
      "text/plain": [
       "  SampleID SubmissionType  SampleNumber SampleIDValidation\n",
       "1 204_V5   Primary in Tube 1            NA                \n",
       "2 226_V1   Primary in Tube 2            NA                \n",
       "3 107_V3   Primary in Tube 3            NA                \n",
       "4 108_V3   Primary in Tube 4            NA                \n",
       "5 109_V1   Primary in Tube 5            NA                \n",
       "6 108_V2   Primary in Tube 6            NA                \n",
       "  DiversigenCheckInSampleName BoxLocation SampleType SampleSource\n",
       "1 204_S                       Box 7, A1   Stool      Human Infant\n",
       "2 NA                          Box 7, A2   Stool      Human Infant\n",
       "3 NA                          Box 7, A3   Stool      Human Infant\n",
       "4 NA                          Box 7, A4   Stool      Human Infant\n",
       "5 NA                          Box 7, A5   Stool      Human Infant\n",
       "6 NA                          Box 7, A6   Stool      Human Infant\n",
       "  SequencingType BabyN ⋯ species_richness ko_shannon ko_evenness ko_richness\n",
       "1 MetaG          204   ⋯ 262               9.631071  0.06754679  3552       \n",
       "2 MetaG          226   ⋯ 143              11.065302  0.45952748  3481       \n",
       "3 MetaG          107   ⋯ 267              10.381667  0.20224759  3664       \n",
       "4 MetaG          108   ⋯ 581              10.545280  0.26200950  3485       \n",
       "5 MetaG          109   ⋯ 193               9.624191  0.11626061  2458       \n",
       "6 MetaG          108   ⋯ 178               9.220455  0.10696066  2822       \n",
       "  Kraken_MDS1 Kraken_MDS2 Kraken_MDS3 KO_MDS1      KO_MDS2    KO_MDS3    \n",
       "1  0.04696486 -0.6813761  -0.1710681   0.361944587 -0.2445720  0.13084552\n",
       "2 -1.68264332 -0.2294583  -0.1636196  -0.808974891 -0.1701531  0.05333913\n",
       "3 -0.27366582 -0.8886653  -0.3349565   0.004286036 -0.2128138 -0.38590665\n",
       "4 -0.33309773  0.7469011   0.4898332  -0.054777557  0.5141869 -0.06048777\n",
       "5 -0.80339905 -0.6660686  -0.5369415   0.367008649 -0.2352781  0.17121560\n",
       "6 -0.99773114  1.0968788   0.3873633   0.193437912  0.7153323  0.55921270"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stool_data = read_csv('../data/metadata/stool/stool_metadata.csv') %>%\n",
    "                 left_join(read_csv('../data/metadata/stool/stool_abx_usage.csv'), by = 'SampleID') %>%\n",
    "                 left_join(read_csv('../data/metadata/stool/stool_titers_yr1.csv'), by = 'SampleID') %>%\n",
    "                 left_join(read_csv('../data/stool/kraken_alpha_diversity.csv'), by = 'SampleID') %>%\n",
    "                 left_join(read_csv('../data/stool/ko_alpha_diversity.csv'), by = 'SampleID')\n",
    "stool_kraken_nmds = read_csv('../data/stool/kraken_nmds_babies.csv') %>% rename_with( ~ paste0(\"Kraken_\", .x), -SampleID)\n",
    "stool_ko_nmds = read_csv('../data/stool/ko_nmds_babies.csv') %>% rename_with( ~ paste0(\"KO_\", .x), -SampleID)\n",
    "stool_data = stool_data %>% left_join(stool_kraken_nmds, by = 'SampleID') %>% left_join(stool_ko_nmds, by = 'SampleID')\n",
    "stool_data = stool_data %>% mutate(VR_group = ifelse(is.na(VR_group), 'Not Measured', VR_group))\n",
    "stool_data = stool_data %>% filter(gt_2.5 == TRUE)\n",
    "stool_data %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "922d4859-8393-4934-8b18-ee74d8de09dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m1008\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m23\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m  (10): SubmissionType, SampleID, DiversigenCheckInSampleName, ReplacesLo...\n",
      "\u001b[32mdbl\u001b[39m   (6): SampleNumber, SequencingType, BabyN, Plate, Row, Column\n",
      "\u001b[33mlgl\u001b[39m   (5): SampleIDValidation, BabyN_checked, DOB_checked, CollectionDate_ch...\n",
      "\u001b[34mdate\u001b[39m  (2): DOB, CollectionDate\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m1008\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m6\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): SampleID\n",
      "\u001b[32mdbl\u001b[39m (2): days_since_abx_start, days_since_abx_end\n",
      "\u001b[33mlgl\u001b[39m (3): never_abx, on_abx, previous_abx\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m826\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m57\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m  (3): SampleID, VR_group, VR_group_v2\n",
      "\u001b[32mdbl\u001b[39m (48): PT, Dip, FHA, PRN, TET, PRP (Hib), PCV ST1, PCV ST3, PCV ST4, PCV ...\n",
      "\u001b[33mlgl\u001b[39m  (6): PT_protected, Dip_protected, FHA_protected, PRN_protected, TET_pro...\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m980\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m4\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): SampleID\n",
      "\u001b[32mdbl\u001b[39m (3): shannon_div, simpson_e_div, n_otus_div\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m944\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m4\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): SampleID\n",
      "\u001b[32mdbl\u001b[39m (3): MDS1, MDS2, MDS3\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1m\u001b[22mJoining, by = \"SampleID\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 91</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>SubmissionType</th><th scope=col>SampleNumber</th><th scope=col>SampleID</th><th scope=col>SampleIDValidation</th><th scope=col>DiversigenCheckInSampleName</th><th scope=col>ReplacesLowVolumeSampleID</th><th scope=col>BoxLocation</th><th scope=col>SampleType</th><th scope=col>SampleSource</th><th scope=col>SequencingType</th><th scope=col>⋯</th><th scope=col>protectNorm_PRN</th><th scope=col>protectNorm_FHA</th><th scope=col>geommean_protectNorm</th><th scope=col>VR_group_v2</th><th scope=col>shannon_div</th><th scope=col>simpson_e_div</th><th scope=col>n_otus_div</th><th scope=col>MDS1</th><th scope=col>MDS2</th><th scope=col>MDS3</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Primary in Tube</td><td>1</td><td>103_V5_NS_A1</td><td>NA</td><td>NA          </td><td>NA</td><td>Box 1, A1</td><td>Nasal Swab</td><td>Human Infant</td><td>16</td><td>⋯</td><td>    NA</td><td>    NA</td><td>       NA</td><td>NA </td><td>1.72045185</td><td>0.07548099</td><td>38</td><td> 0.03476964</td><td>0.2711453</td><td>-0.24546932</td></tr>\n",
       "\t<tr><td>Primary in Tube</td><td>2</td><td>106_V5_NS_A1</td><td>NA</td><td>NA          </td><td>NA</td><td>Box 1, A3</td><td>Nasal Swab</td><td>Human Infant</td><td>16</td><td>⋯</td><td>0.3125</td><td>1.3750</td><td>1.1403884</td><td>NVR</td><td>1.25565101</td><td>0.07274257</td><td>22</td><td>-0.34349902</td><td>0.1522628</td><td> 0.44060459</td></tr>\n",
       "\t<tr><td>Primary in Tube</td><td>3</td><td>107_V2_NS_A1</td><td>NA</td><td>NA          </td><td>NA</td><td>Box 1, A4</td><td>Nasal Swab</td><td>Human Infant</td><td>16</td><td>⋯</td><td>1.1250</td><td>0.3750</td><td>1.7834178</td><td>NVR</td><td>        NA</td><td>        NA</td><td>NA</td><td>         NA</td><td>       NA</td><td>         NA</td></tr>\n",
       "\t<tr><td>Primary in Tube</td><td>4</td><td>107_V3_NS_A1</td><td>NA</td><td>107_V8_NS_A1</td><td>NA</td><td>Box 1, A5</td><td>Nasal Swab</td><td>Human Infant</td><td>16</td><td>⋯</td><td>1.1250</td><td>0.3750</td><td>1.7834178</td><td>NVR</td><td>        NA</td><td>        NA</td><td>NA</td><td>         NA</td><td>       NA</td><td>         NA</td></tr>\n",
       "\t<tr><td>Primary in Tube</td><td>5</td><td>107_V5_NS_A1</td><td>NA</td><td>NA          </td><td>NA</td><td>Box 1, A8</td><td>Nasal Swab</td><td>Human Infant</td><td>16</td><td>⋯</td><td>1.1250</td><td>0.3750</td><td>1.7834178</td><td>NVR</td><td>1.69497733</td><td>0.16593460</td><td>17</td><td>-0.36385022</td><td>0.2648479</td><td>-0.07368183</td></tr>\n",
       "\t<tr><td>Primary in Tube</td><td>6</td><td>108_V4_NS_A1</td><td>NA</td><td>NA          </td><td>NA</td><td>Box 1, A9</td><td>Nasal Swab</td><td>Human Infant</td><td>16</td><td>⋯</td><td>0.3125</td><td>0.1875</td><td>0.4494199</td><td>LVR</td><td>0.05357943</td><td>0.07763519</td><td>13</td><td>-0.50225697</td><td>0.3444246</td><td>-0.10050053</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 91\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " SubmissionType & SampleNumber & SampleID & SampleIDValidation & DiversigenCheckInSampleName & ReplacesLowVolumeSampleID & BoxLocation & SampleType & SampleSource & SequencingType & ⋯ & protectNorm\\_PRN & protectNorm\\_FHA & geommean\\_protectNorm & VR\\_group\\_v2 & shannon\\_div & simpson\\_e\\_div & n\\_otus\\_div & MDS1 & MDS2 & MDS3\\\\\n",
       " <chr> & <dbl> & <chr> & <lgl> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Primary in Tube & 1 & 103\\_V5\\_NS\\_A1 & NA & NA           & NA & Box 1, A1 & Nasal Swab & Human Infant & 16 & ⋯ &     NA &     NA &        NA & NA  & 1.72045185 & 0.07548099 & 38 &  0.03476964 & 0.2711453 & -0.24546932\\\\\n",
       "\t Primary in Tube & 2 & 106\\_V5\\_NS\\_A1 & NA & NA           & NA & Box 1, A3 & Nasal Swab & Human Infant & 16 & ⋯ & 0.3125 & 1.3750 & 1.1403884 & NVR & 1.25565101 & 0.07274257 & 22 & -0.34349902 & 0.1522628 &  0.44060459\\\\\n",
       "\t Primary in Tube & 3 & 107\\_V2\\_NS\\_A1 & NA & NA           & NA & Box 1, A4 & Nasal Swab & Human Infant & 16 & ⋯ & 1.1250 & 0.3750 & 1.7834178 & NVR &         NA &         NA & NA &          NA &        NA &          NA\\\\\n",
       "\t Primary in Tube & 4 & 107\\_V3\\_NS\\_A1 & NA & 107\\_V8\\_NS\\_A1 & NA & Box 1, A5 & Nasal Swab & Human Infant & 16 & ⋯ & 1.1250 & 0.3750 & 1.7834178 & NVR &         NA &         NA & NA &          NA &        NA &          NA\\\\\n",
       "\t Primary in Tube & 5 & 107\\_V5\\_NS\\_A1 & NA & NA           & NA & Box 1, A8 & Nasal Swab & Human Infant & 16 & ⋯ & 1.1250 & 0.3750 & 1.7834178 & NVR & 1.69497733 & 0.16593460 & 17 & -0.36385022 & 0.2648479 & -0.07368183\\\\\n",
       "\t Primary in Tube & 6 & 108\\_V4\\_NS\\_A1 & NA & NA           & NA & Box 1, A9 & Nasal Swab & Human Infant & 16 & ⋯ & 0.3125 & 0.1875 & 0.4494199 & LVR & 0.05357943 & 0.07763519 & 13 & -0.50225697 & 0.3444246 & -0.10050053\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 91\n",
       "\n",
       "| SubmissionType &lt;chr&gt; | SampleNumber &lt;dbl&gt; | SampleID &lt;chr&gt; | SampleIDValidation &lt;lgl&gt; | DiversigenCheckInSampleName &lt;chr&gt; | ReplacesLowVolumeSampleID &lt;chr&gt; | BoxLocation &lt;chr&gt; | SampleType &lt;chr&gt; | SampleSource &lt;chr&gt; | SequencingType &lt;dbl&gt; | ⋯ ⋯ | protectNorm_PRN &lt;dbl&gt; | protectNorm_FHA &lt;dbl&gt; | geommean_protectNorm &lt;dbl&gt; | VR_group_v2 &lt;chr&gt; | shannon_div &lt;dbl&gt; | simpson_e_div &lt;dbl&gt; | n_otus_div &lt;dbl&gt; | MDS1 &lt;dbl&gt; | MDS2 &lt;dbl&gt; | MDS3 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Primary in Tube | 1 | 103_V5_NS_A1 | NA | NA           | NA | Box 1, A1 | Nasal Swab | Human Infant | 16 | ⋯ |     NA |     NA |        NA | NA  | 1.72045185 | 0.07548099 | 38 |  0.03476964 | 0.2711453 | -0.24546932 |\n",
       "| Primary in Tube | 2 | 106_V5_NS_A1 | NA | NA           | NA | Box 1, A3 | Nasal Swab | Human Infant | 16 | ⋯ | 0.3125 | 1.3750 | 1.1403884 | NVR | 1.25565101 | 0.07274257 | 22 | -0.34349902 | 0.1522628 |  0.44060459 |\n",
       "| Primary in Tube | 3 | 107_V2_NS_A1 | NA | NA           | NA | Box 1, A4 | Nasal Swab | Human Infant | 16 | ⋯ | 1.1250 | 0.3750 | 1.7834178 | NVR |         NA |         NA | NA |          NA |        NA |          NA |\n",
       "| Primary in Tube | 4 | 107_V3_NS_A1 | NA | 107_V8_NS_A1 | NA | Box 1, A5 | Nasal Swab | Human Infant | 16 | ⋯ | 1.1250 | 0.3750 | 1.7834178 | NVR |         NA |         NA | NA |          NA |        NA |          NA |\n",
       "| Primary in Tube | 5 | 107_V5_NS_A1 | NA | NA           | NA | Box 1, A8 | Nasal Swab | Human Infant | 16 | ⋯ | 1.1250 | 0.3750 | 1.7834178 | NVR | 1.69497733 | 0.16593460 | 17 | -0.36385022 | 0.2648479 | -0.07368183 |\n",
       "| Primary in Tube | 6 | 108_V4_NS_A1 | NA | NA           | NA | Box 1, A9 | Nasal Swab | Human Infant | 16 | ⋯ | 0.3125 | 0.1875 | 0.4494199 | LVR | 0.05357943 | 0.07763519 | 13 | -0.50225697 | 0.3444246 | -0.10050053 |\n",
       "\n"
      ],
      "text/plain": [
       "  SubmissionType  SampleNumber SampleID     SampleIDValidation\n",
       "1 Primary in Tube 1            103_V5_NS_A1 NA                \n",
       "2 Primary in Tube 2            106_V5_NS_A1 NA                \n",
       "3 Primary in Tube 3            107_V2_NS_A1 NA                \n",
       "4 Primary in Tube 4            107_V3_NS_A1 NA                \n",
       "5 Primary in Tube 5            107_V5_NS_A1 NA                \n",
       "6 Primary in Tube 6            108_V4_NS_A1 NA                \n",
       "  DiversigenCheckInSampleName ReplacesLowVolumeSampleID BoxLocation SampleType\n",
       "1 NA                          NA                        Box 1, A1   Nasal Swab\n",
       "2 NA                          NA                        Box 1, A3   Nasal Swab\n",
       "3 NA                          NA                        Box 1, A4   Nasal Swab\n",
       "4 107_V8_NS_A1                NA                        Box 1, A5   Nasal Swab\n",
       "5 NA                          NA                        Box 1, A8   Nasal Swab\n",
       "6 NA                          NA                        Box 1, A9   Nasal Swab\n",
       "  SampleSource SequencingType ⋯ protectNorm_PRN protectNorm_FHA\n",
       "1 Human Infant 16             ⋯     NA              NA         \n",
       "2 Human Infant 16             ⋯ 0.3125          1.3750         \n",
       "3 Human Infant 16             ⋯ 1.1250          0.3750         \n",
       "4 Human Infant 16             ⋯ 1.1250          0.3750         \n",
       "5 Human Infant 16             ⋯ 1.1250          0.3750         \n",
       "6 Human Infant 16             ⋯ 0.3125          0.1875         \n",
       "  geommean_protectNorm VR_group_v2 shannon_div simpson_e_div n_otus_div\n",
       "1        NA            NA          1.72045185  0.07548099    38        \n",
       "2 1.1403884            NVR         1.25565101  0.07274257    22        \n",
       "3 1.7834178            NVR                 NA          NA    NA        \n",
       "4 1.7834178            NVR                 NA          NA    NA        \n",
       "5 1.7834178            NVR         1.69497733  0.16593460    17        \n",
       "6 0.4494199            LVR         0.05357943  0.07763519    13        \n",
       "  MDS1        MDS2      MDS3       \n",
       "1  0.03476964 0.2711453 -0.24546932\n",
       "2 -0.34349902 0.1522628  0.44060459\n",
       "3          NA        NA          NA\n",
       "4          NA        NA          NA\n",
       "5 -0.36385022 0.2648479 -0.07368183\n",
       "6 -0.50225697 0.3444246 -0.10050053"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nasal_data = read_csv('../data/metadata/nasal/nasal_metadata.csv')\n",
    "nasal_data = nasal_data %>% add_column('age_at_collection' = as.double(difftime(nasal_data$CollectionDate, nasal_data$DOB, units='days'))) %>%\n",
    "                 left_join(read_csv('../data/metadata/nasal/nasal_abx_usage.csv'), by='SampleID') %>%\n",
    "                 left_join(read_csv('../data/metadata/nasal/nasal_titers_yr1.csv'), by='SampleID') %>%\n",
    "                 left_join(read_csv('../data/nasal/otu_alpha_diversity.csv'), by = 'SampleID') %>%\n",
    "                 left_join(read_csv('../data/nasal/otu_nmds_babies.csv'))\n",
    "head(nasal_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e142d07-bc98-4d2a-b674-9d1247f96e6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mNew names:\n",
      "\u001b[36m•\u001b[39m `` -> `...1`\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m143\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m737\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m   (1): ...1\n",
      "\u001b[32mdbl\u001b[39m (736): 101_S1, 101_V3, 101_V5, 102_V1, 102_V3, 102_V5, 103_S1, 103_V10, ...\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Raw_Order</th><th scope=col>Sample</th><th scope=col>Abundance</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>101_S1</td><td>0</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>101_V3</td><td>0</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>101_V5</td><td>0</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>102_V1</td><td>0</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>102_V3</td><td>0</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>102_V5</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " Raw\\_Order & Sample & Abundance\\\\\n",
       " <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 101\\_S1 & 0\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 101\\_V3 & 0\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 101\\_V5 & 0\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 102\\_V1 & 0\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 102\\_V3 & 0\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 102\\_V5 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| Raw_Order &lt;chr&gt; | Sample &lt;chr&gt; | Abundance &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 101_S1 | 0 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 101_V3 | 0 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 101_V5 | 0 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 102_V1 | 0 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 102_V3 | 0 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 102_V5 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  Raw_Order                                                          Sample\n",
       "1 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 101_S1\n",
       "2 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 101_V3\n",
       "3 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 101_V5\n",
       "4 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 102_V1\n",
       "5 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 102_V3\n",
       "6 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 102_V5\n",
       "  Abundance\n",
       "1 0        \n",
       "2 0        \n",
       "3 0        \n",
       "4 0        \n",
       "5 0        \n",
       "6 1        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stool_abundance_data = read_tsv('../data/stool/kraken_taxa_level_abunds/kraken_order_abunds.tsv') %>%\n",
    "                           rename('Raw_Order' = `...1`) %>%\n",
    "                           pivot_longer(!Raw_Order, names_to = \"Sample\", values_to = \"Abundance\")\n",
    "stool_abundance_data %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99d22f0e-e9ed-40f6-945d-6da3d36c255d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m9204\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (2): OTU, Taxonomy\n",
      "\u001b[32mdbl\u001b[39m (1): Size\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>OTU</th><th scope=col>Size</th><th scope=col>Raw_Taxonomy</th><th scope=col>Taxonomy</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Otu0001</td><td>21009360</td><td>Bacteria(100);Proteobacteria(100);Gammaproteobacteria(100);Pseudomonadales(100);Moraxellaceae(100);Moraxella(100);      </td><td>Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      </td></tr>\n",
       "\t<tr><td>Otu0002</td><td> 6758489</td><td>Bacteria(100);Firmicutes(100);Bacilli(100);Bacillales(100);Staphylococcaceae(100);Staphylococcus(100);                  </td><td>Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus;                  </td></tr>\n",
       "\t<tr><td>Otu0003</td><td> 4690364</td><td>Bacteria(100);Firmicutes(100);Bacilli(100);Lactobacillales(100);Streptococcaceae(100);Streptococcus(100);               </td><td>Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;               </td></tr>\n",
       "\t<tr><td>Otu0004</td><td> 4257515</td><td>Bacteria(100);Actinobacteria(100);Actinobacteria(100);Actinomycetales(100);Corynebacteriaceae(100);Corynebacterium(100);</td><td>Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Corynebacteriaceae;Corynebacterium;</td></tr>\n",
       "\t<tr><td>Otu0005</td><td> 4038236</td><td>Bacteria(100);Proteobacteria(100);Gammaproteobacteria(100);Pseudomonadales(100);Moraxellaceae(100);Moraxella(100);      </td><td>Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      </td></tr>\n",
       "\t<tr><td>Otu0006</td><td> 4036658</td><td>Bacteria(100);Firmicutes(100);Bacilli(100);Lactobacillales(100);Carnobacteriaceae(100);Dolosigranulum(100);             </td><td>Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Dolosigranulum;             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " OTU & Size & Raw\\_Taxonomy & Taxonomy\\\\\n",
       " <chr> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Otu0001 & 21009360 & Bacteria(100);Proteobacteria(100);Gammaproteobacteria(100);Pseudomonadales(100);Moraxellaceae(100);Moraxella(100);       & Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      \\\\\n",
       "\t Otu0002 &  6758489 & Bacteria(100);Firmicutes(100);Bacilli(100);Bacillales(100);Staphylococcaceae(100);Staphylococcus(100);                   & Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus;                  \\\\\n",
       "\t Otu0003 &  4690364 & Bacteria(100);Firmicutes(100);Bacilli(100);Lactobacillales(100);Streptococcaceae(100);Streptococcus(100);                & Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;               \\\\\n",
       "\t Otu0004 &  4257515 & Bacteria(100);Actinobacteria(100);Actinobacteria(100);Actinomycetales(100);Corynebacteriaceae(100);Corynebacterium(100); & Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Corynebacteriaceae;Corynebacterium;\\\\\n",
       "\t Otu0005 &  4038236 & Bacteria(100);Proteobacteria(100);Gammaproteobacteria(100);Pseudomonadales(100);Moraxellaceae(100);Moraxella(100);       & Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      \\\\\n",
       "\t Otu0006 &  4036658 & Bacteria(100);Firmicutes(100);Bacilli(100);Lactobacillales(100);Carnobacteriaceae(100);Dolosigranulum(100);              & Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Dolosigranulum;             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 4\n",
       "\n",
       "| OTU &lt;chr&gt; | Size &lt;dbl&gt; | Raw_Taxonomy &lt;chr&gt; | Taxonomy &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| Otu0001 | 21009360 | Bacteria(100);Proteobacteria(100);Gammaproteobacteria(100);Pseudomonadales(100);Moraxellaceae(100);Moraxella(100);       | Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;       |\n",
       "| Otu0002 |  6758489 | Bacteria(100);Firmicutes(100);Bacilli(100);Bacillales(100);Staphylococcaceae(100);Staphylococcus(100);                   | Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus;                   |\n",
       "| Otu0003 |  4690364 | Bacteria(100);Firmicutes(100);Bacilli(100);Lactobacillales(100);Streptococcaceae(100);Streptococcus(100);                | Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;                |\n",
       "| Otu0004 |  4257515 | Bacteria(100);Actinobacteria(100);Actinobacteria(100);Actinomycetales(100);Corynebacteriaceae(100);Corynebacterium(100); | Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Corynebacteriaceae;Corynebacterium; |\n",
       "| Otu0005 |  4038236 | Bacteria(100);Proteobacteria(100);Gammaproteobacteria(100);Pseudomonadales(100);Moraxellaceae(100);Moraxella(100);       | Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;       |\n",
       "| Otu0006 |  4036658 | Bacteria(100);Firmicutes(100);Bacilli(100);Lactobacillales(100);Carnobacteriaceae(100);Dolosigranulum(100);              | Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Dolosigranulum;              |\n",
       "\n"
      ],
      "text/plain": [
       "  OTU     Size    \n",
       "1 Otu0001 21009360\n",
       "2 Otu0002  6758489\n",
       "3 Otu0003  4690364\n",
       "4 Otu0004  4257515\n",
       "5 Otu0005  4038236\n",
       "6 Otu0006  4036658\n",
       "  Raw_Taxonomy                                                                                                            \n",
       "1 Bacteria(100);Proteobacteria(100);Gammaproteobacteria(100);Pseudomonadales(100);Moraxellaceae(100);Moraxella(100);      \n",
       "2 Bacteria(100);Firmicutes(100);Bacilli(100);Bacillales(100);Staphylococcaceae(100);Staphylococcus(100);                  \n",
       "3 Bacteria(100);Firmicutes(100);Bacilli(100);Lactobacillales(100);Streptococcaceae(100);Streptococcus(100);               \n",
       "4 Bacteria(100);Actinobacteria(100);Actinobacteria(100);Actinomycetales(100);Corynebacteriaceae(100);Corynebacterium(100);\n",
       "5 Bacteria(100);Proteobacteria(100);Gammaproteobacteria(100);Pseudomonadales(100);Moraxellaceae(100);Moraxella(100);      \n",
       "6 Bacteria(100);Firmicutes(100);Bacilli(100);Lactobacillales(100);Carnobacteriaceae(100);Dolosigranulum(100);             \n",
       "  Taxonomy                                                                                  \n",
       "1 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      \n",
       "2 Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus;                  \n",
       "3 Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;               \n",
       "4 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Corynebacteriaceae;Corynebacterium;\n",
       "5 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      \n",
       "6 Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Dolosigranulum;             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nasal_taxonomy = read_tsv('../data/nasal/otu_taxonomy.tsv') %>% rename(Raw_Taxonomy = Taxonomy) %>%\n",
    "                     mutate(Taxonomy = str_replace_all(Raw_Taxonomy, '\\\\(\\\\d*\\\\);', ';')) # MTS: I don't understand why you need double backslashes\n",
    "nasal_taxonomy %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36ae4023-2af3-45e6-9eb9-d81a059e9a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sample</th><th scope=col>OTU</th><th scope=col>Abundance</th><th scope=col>Taxonomy</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0001</td><td>   1</td><td>Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      </td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0002</td><td>5845</td><td>Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus;                  </td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0003</td><td> 117</td><td>Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;               </td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0004</td><td>   9</td><td>Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Corynebacteriaceae;Corynebacterium;</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0005</td><td>   0</td><td>Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      </td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0006</td><td>2479</td><td>Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Dolosigranulum;             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Sample & OTU & Abundance & Taxonomy\\\\\n",
       " <chr> & <chr> & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0001 &    1 & Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      \\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0002 & 5845 & Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus;                  \\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0003 &  117 & Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;               \\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0004 &    9 & Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Corynebacteriaceae;Corynebacterium;\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0005 &    0 & Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      \\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0006 & 2479 & Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Dolosigranulum;             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 4\n",
       "\n",
       "| Sample &lt;chr&gt; | OTU &lt;chr&gt; | Abundance &lt;int&gt; | Taxonomy &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 101_S1_NS_A1 | Otu0001 |    1 | Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;       |\n",
       "| 101_S1_NS_A1 | Otu0002 | 5845 | Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus;                   |\n",
       "| 101_S1_NS_A1 | Otu0003 |  117 | Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;                |\n",
       "| 101_S1_NS_A1 | Otu0004 |    9 | Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Corynebacteriaceae;Corynebacterium; |\n",
       "| 101_S1_NS_A1 | Otu0005 |    0 | Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;       |\n",
       "| 101_S1_NS_A1 | Otu0006 | 2479 | Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Dolosigranulum;              |\n",
       "\n"
      ],
      "text/plain": [
       "  Sample       OTU     Abundance\n",
       "1 101_S1_NS_A1 Otu0001    1     \n",
       "2 101_S1_NS_A1 Otu0002 5845     \n",
       "3 101_S1_NS_A1 Otu0003  117     \n",
       "4 101_S1_NS_A1 Otu0004    9     \n",
       "5 101_S1_NS_A1 Otu0005    0     \n",
       "6 101_S1_NS_A1 Otu0006 2479     \n",
       "  Taxonomy                                                                                  \n",
       "1 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      \n",
       "2 Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Staphylococcus;                  \n",
       "3 Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;               \n",
       "4 Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Corynebacteriaceae;Corynebacterium;\n",
       "5 Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;      \n",
       "6 Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Dolosigranulum;             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nasal_abundance_data = as_tibble(read.delim('../data/nasal/otu_table.gt10_rar10K.tsv'), rownames = 'Sample') %>% \n",
    "                           pivot_longer(!Sample, names_to = \"OTU\", values_to = \"Abundance\")\n",
    "nasal_abundance_data = nasal_abundance_data %>% left_join(select(nasal_taxonomy, c('OTU', 'Taxonomy')), by='OTU')\n",
    "nasal_abundance_data %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18fbbe4e-7fe0-46b4-97ae-39e5ce1dad73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m244\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m12\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m  (8): PrimaryKey, BabyN, Name, Reason, Start_Date, End_Date, Duration_(d...\n",
      "\u001b[32mdbl\u001b[39m  (3): AntibioticN, AgeAtStart, AgeAtEnd\n",
      "\u001b[34mdate\u001b[39m (1): DateOfBirth\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PrimaryKey</th><th scope=col>BabyN</th><th scope=col>AntibioticN</th><th scope=col>Name</th><th scope=col>Reason</th><th scope=col>Start_Date</th><th scope=col>End_Date</th><th scope=col>Duration_(days)</th><th scope=col>DateOfBirth</th><th scope=col>AgeAtStart</th><th scope=col>AgeAtEnd</th><th scope=col>Route</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Baby134_Antibiotic1</td><td>Baby134</td><td>1</td><td>piperacillin/ tazobactam</td><td>R/O sepsis                             </td><td>2018-11-26</td><td>2018-11-28    </td><td>2      </td><td>2018-11-22</td><td>4</td><td> 6</td><td>IV</td></tr>\n",
       "\t<tr><td>Baby134_Antibiotic2</td><td>Baby134</td><td>2</td><td>ampicillin              </td><td>R/O sepsis                             </td><td>2018-11-26</td><td>2018-11-27    </td><td>1      </td><td>2018-11-22</td><td>4</td><td> 5</td><td>IV</td></tr>\n",
       "\t<tr><td>Baby134_Antibiotic3</td><td>Baby134</td><td>3</td><td>gentamicin              </td><td>R/O sepsis                             </td><td>2018-11-26</td><td>2018-11-27    </td><td>1      </td><td>2018-11-22</td><td>4</td><td> 5</td><td>IV</td></tr>\n",
       "\t<tr><td>Baby134_Antibiotic4</td><td>Baby134</td><td>4</td><td>vancomycin              </td><td>R/O sepsis                             </td><td>2018-11-26</td><td>2018-11-27    </td><td>1      </td><td>2018-11-22</td><td>4</td><td> 5</td><td>IV</td></tr>\n",
       "\t<tr><td>Baby235_Antibiotic1</td><td>Baby235</td><td>1</td><td>unknown anitbiotic(s)   </td><td>R/O sepsis, later confirmed neg.       </td><td>2018-06-19</td><td>Not Documented</td><td>Unknown</td><td>2018-06-13</td><td>6</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><td>Baby245_Antibiotic1</td><td>Baby245</td><td>1</td><td>ampicillin              </td><td>respiratory issues, R/O sepsis at birth</td><td>2018-07-23</td><td>2018-07-26    </td><td>3      </td><td>2018-07-22</td><td>1</td><td> 4</td><td>IV</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 12\n",
       "\\begin{tabular}{llllllllllll}\n",
       " PrimaryKey & BabyN & AntibioticN & Name & Reason & Start\\_Date & End\\_Date & Duration\\_(days) & DateOfBirth & AgeAtStart & AgeAtEnd & Route\\\\\n",
       " <chr> & <chr> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <date> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Baby134\\_Antibiotic1 & Baby134 & 1 & piperacillin/ tazobactam & R/O sepsis                              & 2018-11-26 & 2018-11-28     & 2       & 2018-11-22 & 4 &  6 & IV\\\\\n",
       "\t Baby134\\_Antibiotic2 & Baby134 & 2 & ampicillin               & R/O sepsis                              & 2018-11-26 & 2018-11-27     & 1       & 2018-11-22 & 4 &  5 & IV\\\\\n",
       "\t Baby134\\_Antibiotic3 & Baby134 & 3 & gentamicin               & R/O sepsis                              & 2018-11-26 & 2018-11-27     & 1       & 2018-11-22 & 4 &  5 & IV\\\\\n",
       "\t Baby134\\_Antibiotic4 & Baby134 & 4 & vancomycin               & R/O sepsis                              & 2018-11-26 & 2018-11-27     & 1       & 2018-11-22 & 4 &  5 & IV\\\\\n",
       "\t Baby235\\_Antibiotic1 & Baby235 & 1 & unknown anitbiotic(s)    & R/O sepsis, later confirmed neg.        & 2018-06-19 & Not Documented & Unknown & 2018-06-13 & 6 & NA & NA\\\\\n",
       "\t Baby245\\_Antibiotic1 & Baby245 & 1 & ampicillin               & respiratory issues, R/O sepsis at birth & 2018-07-23 & 2018-07-26     & 3       & 2018-07-22 & 1 &  4 & IV\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 12\n",
       "\n",
       "| PrimaryKey &lt;chr&gt; | BabyN &lt;chr&gt; | AntibioticN &lt;dbl&gt; | Name &lt;chr&gt; | Reason &lt;chr&gt; | Start_Date &lt;chr&gt; | End_Date &lt;chr&gt; | Duration_(days) &lt;chr&gt; | DateOfBirth &lt;date&gt; | AgeAtStart &lt;dbl&gt; | AgeAtEnd &lt;dbl&gt; | Route &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Baby134_Antibiotic1 | Baby134 | 1 | piperacillin/ tazobactam | R/O sepsis                              | 2018-11-26 | 2018-11-28     | 2       | 2018-11-22 | 4 |  6 | IV |\n",
       "| Baby134_Antibiotic2 | Baby134 | 2 | ampicillin               | R/O sepsis                              | 2018-11-26 | 2018-11-27     | 1       | 2018-11-22 | 4 |  5 | IV |\n",
       "| Baby134_Antibiotic3 | Baby134 | 3 | gentamicin               | R/O sepsis                              | 2018-11-26 | 2018-11-27     | 1       | 2018-11-22 | 4 |  5 | IV |\n",
       "| Baby134_Antibiotic4 | Baby134 | 4 | vancomycin               | R/O sepsis                              | 2018-11-26 | 2018-11-27     | 1       | 2018-11-22 | 4 |  5 | IV |\n",
       "| Baby235_Antibiotic1 | Baby235 | 1 | unknown anitbiotic(s)    | R/O sepsis, later confirmed neg.        | 2018-06-19 | Not Documented | Unknown | 2018-06-13 | 6 | NA | NA |\n",
       "| Baby245_Antibiotic1 | Baby245 | 1 | ampicillin               | respiratory issues, R/O sepsis at birth | 2018-07-23 | 2018-07-26     | 3       | 2018-07-22 | 1 |  4 | IV |\n",
       "\n"
      ],
      "text/plain": [
       "  PrimaryKey          BabyN   AntibioticN Name                    \n",
       "1 Baby134_Antibiotic1 Baby134 1           piperacillin/ tazobactam\n",
       "2 Baby134_Antibiotic2 Baby134 2           ampicillin              \n",
       "3 Baby134_Antibiotic3 Baby134 3           gentamicin              \n",
       "4 Baby134_Antibiotic4 Baby134 4           vancomycin              \n",
       "5 Baby235_Antibiotic1 Baby235 1           unknown anitbiotic(s)   \n",
       "6 Baby245_Antibiotic1 Baby245 1           ampicillin              \n",
       "  Reason                                  Start_Date End_Date      \n",
       "1 R/O sepsis                              2018-11-26 2018-11-28    \n",
       "2 R/O sepsis                              2018-11-26 2018-11-27    \n",
       "3 R/O sepsis                              2018-11-26 2018-11-27    \n",
       "4 R/O sepsis                              2018-11-26 2018-11-27    \n",
       "5 R/O sepsis, later confirmed neg.        2018-06-19 Not Documented\n",
       "6 respiratory issues, R/O sepsis at birth 2018-07-23 2018-07-26    \n",
       "  Duration_(days) DateOfBirth AgeAtStart AgeAtEnd Route\n",
       "1 2               2018-11-22  4           6       IV   \n",
       "2 1               2018-11-22  4           5       IV   \n",
       "3 1               2018-11-22  4           5       IV   \n",
       "4 1               2018-11-22  4           5       IV   \n",
       "5 Unknown         2018-06-13  6          NA       NA   \n",
       "6 3               2018-07-22  1           4       IV   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Load abx details\n",
    "# abx_details <- read_delim(\"../../data/metadata/processed_data_sheets/updated_abx_details.csv\")\n",
    "abx_details <- read_delim(\"../data/metadata/antibiotic_usage.tsv\")\n",
    "head(abx_details)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820b53d4-07bd-4780-a113-4811a7c88d99",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Making plots\n",
    "\n",
    "It's all yours Cathy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f59c4270-1a5f-468b-8e86-2635e197286b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sample</th><th scope=col>OTU</th><th scope=col>Abundance</th><th scope=col>Taxonomy</th><th scope=col>BabyN</th><th scope=col>VisitCode</th><th scope=col>age_at_collection</th><th scope=col>days_since_abx_start</th><th scope=col>days_since_abx_end</th><th scope=col>on_abx</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0001</td><td>   1</td><td>Pseudomonadales</td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0002</td><td>5845</td><td>Bacillales     </td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0003</td><td> 117</td><td>Lactobacillales</td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0004</td><td>   9</td><td>Actinomycetales</td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0005</td><td>   0</td><td>Pseudomonadales</td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0006</td><td>2479</td><td>Lactobacillales</td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " Sample & OTU & Abundance & Taxonomy & BabyN & VisitCode & age\\_at\\_collection & days\\_since\\_abx\\_start & days\\_since\\_abx\\_end & on\\_abx\\\\\n",
       " <chr> & <chr> & <int> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <lgl>\\\\\n",
       "\\hline\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0001 &    1 & Pseudomonadales & 101 & S1 & 44 & NA & NA & FALSE\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0002 & 5845 & Bacillales      & 101 & S1 & 44 & NA & NA & FALSE\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0003 &  117 & Lactobacillales & 101 & S1 & 44 & NA & NA & FALSE\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0004 &    9 & Actinomycetales & 101 & S1 & 44 & NA & NA & FALSE\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0005 &    0 & Pseudomonadales & 101 & S1 & 44 & NA & NA & FALSE\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0006 & 2479 & Lactobacillales & 101 & S1 & 44 & NA & NA & FALSE\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 10\n",
       "\n",
       "| Sample &lt;chr&gt; | OTU &lt;chr&gt; | Abundance &lt;int&gt; | Taxonomy &lt;chr&gt; | BabyN &lt;dbl&gt; | VisitCode &lt;chr&gt; | age_at_collection &lt;dbl&gt; | days_since_abx_start &lt;dbl&gt; | days_since_abx_end &lt;dbl&gt; | on_abx &lt;lgl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 101_S1_NS_A1 | Otu0001 |    1 | Pseudomonadales | 101 | S1 | 44 | NA | NA | FALSE |\n",
       "| 101_S1_NS_A1 | Otu0002 | 5845 | Bacillales      | 101 | S1 | 44 | NA | NA | FALSE |\n",
       "| 101_S1_NS_A1 | Otu0003 |  117 | Lactobacillales | 101 | S1 | 44 | NA | NA | FALSE |\n",
       "| 101_S1_NS_A1 | Otu0004 |    9 | Actinomycetales | 101 | S1 | 44 | NA | NA | FALSE |\n",
       "| 101_S1_NS_A1 | Otu0005 |    0 | Pseudomonadales | 101 | S1 | 44 | NA | NA | FALSE |\n",
       "| 101_S1_NS_A1 | Otu0006 | 2479 | Lactobacillales | 101 | S1 | 44 | NA | NA | FALSE |\n",
       "\n"
      ],
      "text/plain": [
       "  Sample       OTU     Abundance Taxonomy        BabyN VisitCode\n",
       "1 101_S1_NS_A1 Otu0001    1      Pseudomonadales 101   S1       \n",
       "2 101_S1_NS_A1 Otu0002 5845      Bacillales      101   S1       \n",
       "3 101_S1_NS_A1 Otu0003  117      Lactobacillales 101   S1       \n",
       "4 101_S1_NS_A1 Otu0004    9      Actinomycetales 101   S1       \n",
       "5 101_S1_NS_A1 Otu0005    0      Pseudomonadales 101   S1       \n",
       "6 101_S1_NS_A1 Otu0006 2479      Lactobacillales 101   S1       \n",
       "  age_at_collection days_since_abx_start days_since_abx_end on_abx\n",
       "1 44                NA                   NA                 FALSE \n",
       "2 44                NA                   NA                 FALSE \n",
       "3 44                NA                   NA                 FALSE \n",
       "4 44                NA                   NA                 FALSE \n",
       "5 44                NA                   NA                 FALSE \n",
       "6 44                NA                   NA                 FALSE "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Add age of collection\n",
    "nasal_sample_ages <- nasal_data %>% distinct(SampleID, BabyN, VisitCode, on_abx, age_at_collection, days_since_abx_start, days_since_abx_end)\n",
    "\n",
    "nasal_abundance_data <- nasal_abundance_data %>%\n",
    "    left_join(nasal_sample_ages, by=c(\"Sample\"=\"SampleID\"))\n",
    "\n",
    "## Split taxonomy on \";\", grab 4th value\n",
    "nasal_taxa_level <- 4\n",
    "nasal_abundance_data <- nasal_abundance_data %>%\n",
    "    rowwise() %>%\n",
    "    mutate(Taxonomy = strsplit(Taxonomy, \";\", fixed = T)[[1]][nasal_taxa_level]) %>%\n",
    "    ungroup\n",
    "head(nasal_abundance_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ec5a244-6d76-4939-84f4-7ce3691090d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Raw_Order</th><th scope=col>Sample</th><th scope=col>Abundance</th><th scope=col>Taxonomy</th><th scope=col>BabyN</th><th scope=col>VisitCode</th><th scope=col>age_at_collection</th><th scope=col>days_since_abx_start</th><th scope=col>days_since_abx_end</th><th scope=col>on_abx</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>101_S1</td><td>0</td><td>Acidobacteriales</td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>101_V3</td><td>0</td><td>Acidobacteriales</td><td>101</td><td>V3</td><td>23</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>101_V5</td><td>0</td><td>Acidobacteriales</td><td>101</td><td>V5</td><td>61</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>102_V1</td><td>0</td><td>Acidobacteriales</td><td>102</td><td>V1</td><td> 6</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>102_V3</td><td>0</td><td>Acidobacteriales</td><td>102</td><td>V3</td><td>15</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>102_V5</td><td>1</td><td>Acidobacteriales</td><td>102</td><td>V5</td><td>59</td><td>NA</td><td>NA</td><td>FALSE</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " Raw\\_Order & Sample & Abundance & Taxonomy & BabyN & VisitCode & age\\_at\\_collection & days\\_since\\_abx\\_start & days\\_since\\_abx\\_end & on\\_abx\\\\\n",
       " <chr> & <chr> & <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <lgl>\\\\\n",
       "\\hline\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 101\\_S1 & 0 & Acidobacteriales & 101 & S1 & 44 & NA & NA & FALSE\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 101\\_V3 & 0 & Acidobacteriales & 101 & V3 & 23 & NA & NA & FALSE\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 101\\_V5 & 0 & Acidobacteriales & 101 & V5 & 61 & NA & NA & FALSE\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 102\\_V1 & 0 & Acidobacteriales & 102 & V1 &  6 & NA & NA & FALSE\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 102\\_V3 & 0 & Acidobacteriales & 102 & V3 & 15 & NA & NA & FALSE\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 102\\_V5 & 1 & Acidobacteriales & 102 & V5 & 59 & NA & NA & FALSE\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 10\n",
       "\n",
       "| Raw_Order &lt;chr&gt; | Sample &lt;chr&gt; | Abundance &lt;dbl&gt; | Taxonomy &lt;chr&gt; | BabyN &lt;dbl&gt; | VisitCode &lt;chr&gt; | age_at_collection &lt;dbl&gt; | days_since_abx_start &lt;dbl&gt; | days_since_abx_end &lt;dbl&gt; | on_abx &lt;lgl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 101_S1 | 0 | Acidobacteriales | 101 | S1 | 44 | NA | NA | FALSE |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 101_V3 | 0 | Acidobacteriales | 101 | V3 | 23 | NA | NA | FALSE |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 101_V5 | 0 | Acidobacteriales | 101 | V5 | 61 | NA | NA | FALSE |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 102_V1 | 0 | Acidobacteriales | 102 | V1 |  6 | NA | NA | FALSE |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 102_V3 | 0 | Acidobacteriales | 102 | V3 | 15 | NA | NA | FALSE |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 102_V5 | 1 | Acidobacteriales | 102 | V5 | 59 | NA | NA | FALSE |\n",
       "\n"
      ],
      "text/plain": [
       "  Raw_Order                                                          Sample\n",
       "1 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 101_S1\n",
       "2 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 101_V3\n",
       "3 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 101_V5\n",
       "4 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 102_V1\n",
       "5 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 102_V3\n",
       "6 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 102_V5\n",
       "  Abundance Taxonomy         BabyN VisitCode age_at_collection\n",
       "1 0         Acidobacteriales 101   S1        44               \n",
       "2 0         Acidobacteriales 101   V3        23               \n",
       "3 0         Acidobacteriales 101   V5        61               \n",
       "4 0         Acidobacteriales 102   V1         6               \n",
       "5 0         Acidobacteriales 102   V3        15               \n",
       "6 1         Acidobacteriales 102   V5        59               \n",
       "  days_since_abx_start days_since_abx_end on_abx\n",
       "1 NA                   NA                 FALSE \n",
       "2 NA                   NA                 FALSE \n",
       "3 NA                   NA                 FALSE \n",
       "4 NA                   NA                 FALSE \n",
       "5 NA                   NA                 FALSE \n",
       "6 NA                   NA                 FALSE "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Add kraken column: split taxonomy on \"|\", grab 4th value and remove \"o__\"\n",
    "stool_taxa_level <- 4\n",
    "stool_abundance_data <- stool_abundance_data %>%\n",
    "    rowwise() %>%\n",
    "    mutate(Taxonomy = strsplit(Raw_Order, \"|\", fixed=T)[[1]][stool_taxa_level]) %>%\n",
    "    ungroup\n",
    "\n",
    "## Remove \"o__\" from string\n",
    "stool_abundance_data$Taxonomy <- gsub(\"o__\", \"\", stool_abundance_data$Taxonomy)\n",
    "## Add age of collection and whether or not baby was taking antibiotics\n",
    "stool_sample_ages <- stool_data %>%\n",
    "    distinct(SampleID, BabyN, on_abx, VisitCode, age_at_collection, days_since_abx_start, days_since_abx_end)\n",
    "\n",
    "stool_abundance_data <- stool_abundance_data %>%\n",
    "    left_join(stool_sample_ages, by=c(\"Sample\"=\"SampleID\"))\n",
    "\n",
    "\n",
    "head(stool_abundance_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db97a6a7-d5ef-4404-8cc9-4302a5b6b032",
   "metadata": {},
   "outputs": [],
   "source": [
    "summarize_low_to_other <- function(abunds, other_fraction=0.01) {\n",
    "    ## Label taxa as \"Other\" if below fractional abundance in ALL samples\n",
    "    abunds %>%\n",
    "    group_by(Sample) %>%\n",
    "    mutate(rel_abund = Abundance / sum(Abundance)) %>%\n",
    "    ungroup %>%\n",
    "    group_by(Taxonomy) %>%\n",
    "    rowwise() %>%\n",
    "    mutate(Taxonomy = ifelse( all(rel_abund < other_fraction), \"Other\", Taxonomy )) %>%\n",
    "    ungroup %>%\n",
    "    return\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b932b7ef-4932-4c1a-952b-56a91c72cdf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sample</th><th scope=col>OTU</th><th scope=col>Abundance</th><th scope=col>Taxonomy</th><th scope=col>BabyN</th><th scope=col>VisitCode</th><th scope=col>age_at_collection</th><th scope=col>days_since_abx_start</th><th scope=col>days_since_abx_end</th><th scope=col>on_abx</th><th scope=col>rel_abund</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0001</td><td>   1</td><td>Other          </td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0001</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0002</td><td>5845</td><td>Bacillales     </td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.5845</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0003</td><td> 117</td><td>Other          </td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0117</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0004</td><td>   9</td><td>Other          </td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0009</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0005</td><td>   0</td><td>Other          </td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0000</td></tr>\n",
       "\t<tr><td>101_S1_NS_A1</td><td>Otu0006</td><td>2479</td><td>Lactobacillales</td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.2479</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " Sample & OTU & Abundance & Taxonomy & BabyN & VisitCode & age\\_at\\_collection & days\\_since\\_abx\\_start & days\\_since\\_abx\\_end & on\\_abx & rel\\_abund\\\\\n",
       " <chr> & <chr> & <int> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <lgl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0001 &    1 & Other           & 101 & S1 & 44 & NA & NA & FALSE & 0.0001\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0002 & 5845 & Bacillales      & 101 & S1 & 44 & NA & NA & FALSE & 0.5845\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0003 &  117 & Other           & 101 & S1 & 44 & NA & NA & FALSE & 0.0117\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0004 &    9 & Other           & 101 & S1 & 44 & NA & NA & FALSE & 0.0009\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0005 &    0 & Other           & 101 & S1 & 44 & NA & NA & FALSE & 0.0000\\\\\n",
       "\t 101\\_S1\\_NS\\_A1 & Otu0006 & 2479 & Lactobacillales & 101 & S1 & 44 & NA & NA & FALSE & 0.2479\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 11\n",
       "\n",
       "| Sample &lt;chr&gt; | OTU &lt;chr&gt; | Abundance &lt;int&gt; | Taxonomy &lt;chr&gt; | BabyN &lt;dbl&gt; | VisitCode &lt;chr&gt; | age_at_collection &lt;dbl&gt; | days_since_abx_start &lt;dbl&gt; | days_since_abx_end &lt;dbl&gt; | on_abx &lt;lgl&gt; | rel_abund &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 101_S1_NS_A1 | Otu0001 |    1 | Other           | 101 | S1 | 44 | NA | NA | FALSE | 0.0001 |\n",
       "| 101_S1_NS_A1 | Otu0002 | 5845 | Bacillales      | 101 | S1 | 44 | NA | NA | FALSE | 0.5845 |\n",
       "| 101_S1_NS_A1 | Otu0003 |  117 | Other           | 101 | S1 | 44 | NA | NA | FALSE | 0.0117 |\n",
       "| 101_S1_NS_A1 | Otu0004 |    9 | Other           | 101 | S1 | 44 | NA | NA | FALSE | 0.0009 |\n",
       "| 101_S1_NS_A1 | Otu0005 |    0 | Other           | 101 | S1 | 44 | NA | NA | FALSE | 0.0000 |\n",
       "| 101_S1_NS_A1 | Otu0006 | 2479 | Lactobacillales | 101 | S1 | 44 | NA | NA | FALSE | 0.2479 |\n",
       "\n"
      ],
      "text/plain": [
       "  Sample       OTU     Abundance Taxonomy        BabyN VisitCode\n",
       "1 101_S1_NS_A1 Otu0001    1      Other           101   S1       \n",
       "2 101_S1_NS_A1 Otu0002 5845      Bacillales      101   S1       \n",
       "3 101_S1_NS_A1 Otu0003  117      Other           101   S1       \n",
       "4 101_S1_NS_A1 Otu0004    9      Other           101   S1       \n",
       "5 101_S1_NS_A1 Otu0005    0      Other           101   S1       \n",
       "6 101_S1_NS_A1 Otu0006 2479      Lactobacillales 101   S1       \n",
       "  age_at_collection days_since_abx_start days_since_abx_end on_abx rel_abund\n",
       "1 44                NA                   NA                 FALSE  0.0001   \n",
       "2 44                NA                   NA                 FALSE  0.5845   \n",
       "3 44                NA                   NA                 FALSE  0.0117   \n",
       "4 44                NA                   NA                 FALSE  0.0009   \n",
       "5 44                NA                   NA                 FALSE  0.0000   \n",
       "6 44                NA                   NA                 FALSE  0.2479   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Raw_Order</th><th scope=col>Sample</th><th scope=col>Abundance</th><th scope=col>Taxonomy</th><th scope=col>BabyN</th><th scope=col>VisitCode</th><th scope=col>age_at_collection</th><th scope=col>days_since_abx_start</th><th scope=col>days_since_abx_end</th><th scope=col>on_abx</th><th scope=col>rel_abund</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>101_S1</td><td>0</td><td>Other</td><td>101</td><td>S1</td><td>44</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.000000e+00</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>101_V3</td><td>0</td><td>Other</td><td>101</td><td>V3</td><td>23</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.000000e+00</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>101_V5</td><td>0</td><td>Other</td><td>101</td><td>V5</td><td>61</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.000000e+00</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>102_V1</td><td>0</td><td>Other</td><td>102</td><td>V1</td><td> 6</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.000000e+00</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>102_V3</td><td>0</td><td>Other</td><td>102</td><td>V3</td><td>15</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.000000e+00</td></tr>\n",
       "\t<tr><td>d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales</td><td>102_V5</td><td>1</td><td>Other</td><td>102</td><td>V5</td><td>59</td><td>NA</td><td>NA</td><td>FALSE</td><td>4.576332e-07</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " Raw\\_Order & Sample & Abundance & Taxonomy & BabyN & VisitCode & age\\_at\\_collection & days\\_since\\_abx\\_start & days\\_since\\_abx\\_end & on\\_abx & rel\\_abund\\\\\n",
       " <chr> & <chr> & <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <lgl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 101\\_S1 & 0 & Other & 101 & S1 & 44 & NA & NA & FALSE & 0.000000e+00\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 101\\_V3 & 0 & Other & 101 & V3 & 23 & NA & NA & FALSE & 0.000000e+00\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 101\\_V5 & 0 & Other & 101 & V5 & 61 & NA & NA & FALSE & 0.000000e+00\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 102\\_V1 & 0 & Other & 102 & V1 &  6 & NA & NA & FALSE & 0.000000e+00\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 102\\_V3 & 0 & Other & 102 & V3 & 15 & NA & NA & FALSE & 0.000000e+00\\\\\n",
       "\t d\\_\\_Bacteria\\textbar{}p\\_\\_Acidobacteria\\textbar{}c\\_\\_Acidobacteriia\\textbar{}o\\_\\_Acidobacteriales & 102\\_V5 & 1 & Other & 102 & V5 & 59 & NA & NA & FALSE & 4.576332e-07\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 11\n",
       "\n",
       "| Raw_Order &lt;chr&gt; | Sample &lt;chr&gt; | Abundance &lt;dbl&gt; | Taxonomy &lt;chr&gt; | BabyN &lt;dbl&gt; | VisitCode &lt;chr&gt; | age_at_collection &lt;dbl&gt; | days_since_abx_start &lt;dbl&gt; | days_since_abx_end &lt;dbl&gt; | on_abx &lt;lgl&gt; | rel_abund &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 101_S1 | 0 | Other | 101 | S1 | 44 | NA | NA | FALSE | 0.000000e+00 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 101_V3 | 0 | Other | 101 | V3 | 23 | NA | NA | FALSE | 0.000000e+00 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 101_V5 | 0 | Other | 101 | V5 | 61 | NA | NA | FALSE | 0.000000e+00 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 102_V1 | 0 | Other | 102 | V1 |  6 | NA | NA | FALSE | 0.000000e+00 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 102_V3 | 0 | Other | 102 | V3 | 15 | NA | NA | FALSE | 0.000000e+00 |\n",
       "| d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales | 102_V5 | 1 | Other | 102 | V5 | 59 | NA | NA | FALSE | 4.576332e-07 |\n",
       "\n"
      ],
      "text/plain": [
       "  Raw_Order                                                          Sample\n",
       "1 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 101_S1\n",
       "2 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 101_V3\n",
       "3 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 101_V5\n",
       "4 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 102_V1\n",
       "5 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 102_V3\n",
       "6 d__Bacteria|p__Acidobacteria|c__Acidobacteriia|o__Acidobacteriales 102_V5\n",
       "  Abundance Taxonomy BabyN VisitCode age_at_collection days_since_abx_start\n",
       "1 0         Other    101   S1        44                NA                  \n",
       "2 0         Other    101   V3        23                NA                  \n",
       "3 0         Other    101   V5        61                NA                  \n",
       "4 0         Other    102   V1         6                NA                  \n",
       "5 0         Other    102   V3        15                NA                  \n",
       "6 1         Other    102   V5        59                NA                  \n",
       "  days_since_abx_end on_abx rel_abund   \n",
       "1 NA                 FALSE  0.000000e+00\n",
       "2 NA                 FALSE  0.000000e+00\n",
       "3 NA                 FALSE  0.000000e+00\n",
       "4 NA                 FALSE  0.000000e+00\n",
       "5 NA                 FALSE  0.000000e+00\n",
       "6 NA                 FALSE  4.576332e-07"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Calculate relative abundance for nasal and stool\n",
    "nasal_rel_abunds <- summarize_low_to_other(nasal_abundance_data, 0.1)\n",
    "head(nasal_rel_abunds)\n",
    "\n",
    "stool_rel_abunds <- summarize_low_to_other(stool_abundance_data, 0.1)\n",
    "head(stool_rel_abunds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7bf8e04c-40e1-41db-b431-6b631da392d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sample</th><th scope=col>Abundance</th><th scope=col>Taxonomy</th><th scope=col>BabyN</th><th scope=col>VisitCode</th><th scope=col>age_at_collection</th><th scope=col>days_since_abx_start</th><th scope=col>days_since_abx_end</th><th scope=col>on_abx</th><th scope=col>rel_abund</th><th scope=col>stool_or_nasal</th><th scope=col>visit_category</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>101_V3</td><td>   0</td><td>Other     </td><td>101</td><td>V3</td><td>23</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0000</td><td>Nasal</td><td>Well</td></tr>\n",
       "\t<tr><td>101_V3</td><td>9876</td><td>Bacillales</td><td>101</td><td>V3</td><td>23</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.9876</td><td>Nasal</td><td>Well</td></tr>\n",
       "\t<tr><td>101_V3</td><td>   0</td><td>Other     </td><td>101</td><td>V3</td><td>23</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0000</td><td>Nasal</td><td>Well</td></tr>\n",
       "\t<tr><td>101_V3</td><td>   1</td><td>Other     </td><td>101</td><td>V3</td><td>23</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0001</td><td>Nasal</td><td>Well</td></tr>\n",
       "\t<tr><td>101_V3</td><td>   0</td><td>Other     </td><td>101</td><td>V3</td><td>23</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0000</td><td>Nasal</td><td>Well</td></tr>\n",
       "\t<tr><td>101_V3</td><td>   0</td><td>Other     </td><td>101</td><td>V3</td><td>23</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0000</td><td>Nasal</td><td>Well</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 12\n",
       "\\begin{tabular}{llllllllllll}\n",
       " Sample & Abundance & Taxonomy & BabyN & VisitCode & age\\_at\\_collection & days\\_since\\_abx\\_start & days\\_since\\_abx\\_end & on\\_abx & rel\\_abund & stool\\_or\\_nasal & visit\\_category\\\\\n",
       " <chr> & <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <lgl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 101\\_V3 &    0 & Other      & 101 & V3 & 23 & NA & NA & FALSE & 0.0000 & Nasal & Well\\\\\n",
       "\t 101\\_V3 & 9876 & Bacillales & 101 & V3 & 23 & NA & NA & FALSE & 0.9876 & Nasal & Well\\\\\n",
       "\t 101\\_V3 &    0 & Other      & 101 & V3 & 23 & NA & NA & FALSE & 0.0000 & Nasal & Well\\\\\n",
       "\t 101\\_V3 &    1 & Other      & 101 & V3 & 23 & NA & NA & FALSE & 0.0001 & Nasal & Well\\\\\n",
       "\t 101\\_V3 &    0 & Other      & 101 & V3 & 23 & NA & NA & FALSE & 0.0000 & Nasal & Well\\\\\n",
       "\t 101\\_V3 &    0 & Other      & 101 & V3 & 23 & NA & NA & FALSE & 0.0000 & Nasal & Well\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 12\n",
       "\n",
       "| Sample &lt;chr&gt; | Abundance &lt;dbl&gt; | Taxonomy &lt;chr&gt; | BabyN &lt;dbl&gt; | VisitCode &lt;chr&gt; | age_at_collection &lt;dbl&gt; | days_since_abx_start &lt;dbl&gt; | days_since_abx_end &lt;dbl&gt; | on_abx &lt;lgl&gt; | rel_abund &lt;dbl&gt; | stool_or_nasal &lt;chr&gt; | visit_category &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 101_V3 |    0 | Other      | 101 | V3 | 23 | NA | NA | FALSE | 0.0000 | Nasal | Well |\n",
       "| 101_V3 | 9876 | Bacillales | 101 | V3 | 23 | NA | NA | FALSE | 0.9876 | Nasal | Well |\n",
       "| 101_V3 |    0 | Other      | 101 | V3 | 23 | NA | NA | FALSE | 0.0000 | Nasal | Well |\n",
       "| 101_V3 |    1 | Other      | 101 | V3 | 23 | NA | NA | FALSE | 0.0001 | Nasal | Well |\n",
       "| 101_V3 |    0 | Other      | 101 | V3 | 23 | NA | NA | FALSE | 0.0000 | Nasal | Well |\n",
       "| 101_V3 |    0 | Other      | 101 | V3 | 23 | NA | NA | FALSE | 0.0000 | Nasal | Well |\n",
       "\n"
      ],
      "text/plain": [
       "  Sample Abundance Taxonomy   BabyN VisitCode age_at_collection\n",
       "1 101_V3    0      Other      101   V3        23               \n",
       "2 101_V3 9876      Bacillales 101   V3        23               \n",
       "3 101_V3    0      Other      101   V3        23               \n",
       "4 101_V3    1      Other      101   V3        23               \n",
       "5 101_V3    0      Other      101   V3        23               \n",
       "6 101_V3    0      Other      101   V3        23               \n",
       "  days_since_abx_start days_since_abx_end on_abx rel_abund stool_or_nasal\n",
       "1 NA                   NA                 FALSE  0.0000    Nasal         \n",
       "2 NA                   NA                 FALSE  0.9876    Nasal         \n",
       "3 NA                   NA                 FALSE  0.0000    Nasal         \n",
       "4 NA                   NA                 FALSE  0.0001    Nasal         \n",
       "5 NA                   NA                 FALSE  0.0000    Nasal         \n",
       "6 NA                   NA                 FALSE  0.0000    Nasal         \n",
       "  visit_category\n",
       "1 Well          \n",
       "2 Well          \n",
       "3 Well          \n",
       "4 Well          \n",
       "5 Well          \n",
       "6 Well          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Fix Sample colunms and add nasal category\n",
    "nasal <- nasal_rel_abunds %>%\n",
    "    mutate(stool_or_nasal = \"Nasal\",\n",
    "           Sample = gsub(\"_NS.*\", \"\", Sample)\n",
    "          ) %>%\n",
    "    arrange(age_at_collection) %>%\n",
    "    select(-OTU)\n",
    "\n",
    "stool <- stool_rel_abunds %>%\n",
    "    mutate(stool_or_nasal = \"Stool\") %>%\n",
    "    select(-Raw_Order)\n",
    "\n",
    "## Combined nasal and stool data frames\n",
    "nasal_stool_combined <- bind_rows(nasal, stool)\n",
    "\n",
    "## Choose one age_at_collection per sample (if multiple are present)\n",
    "nasal_stool_combined <- nasal_stool_combined %>%\n",
    "    group_by(stool_or_nasal, BabyN, age_at_collection) %>%\n",
    "    slice_min(Sample) %>%\n",
    "    ungroup\n",
    "\n",
    "## Add visit category\n",
    "nasal_stool_combined <- nasal_stool_combined %>%\n",
    "    mutate(visit_category = case_when(\n",
    "        grepl(\"F\", VisitCode) ~ \"Follow-up\",\n",
    "        grepl(\"S\", VisitCode) ~ \"Sick\",\n",
    "        grepl(\"A\", VisitCode) ~ \"AOM\",\n",
    "        grepl(\"V\", VisitCode) ~ \"Well\",\n",
    "        TRUE ~ NA_character_\n",
    "    )\n",
    "          )\n",
    "           \n",
    "head(nasal_stool_combined)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19f0b672-0472-4d19-9fed-eeb83c311b0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mJoining, by = c(\"Sample\", \"age_at_collection\", \"stool_or_nasal\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sample</th><th scope=col>Abundance</th><th scope=col>Taxonomy</th><th scope=col>BabyN</th><th scope=col>VisitCode</th><th scope=col>age_at_collection</th><th scope=col>days_since_abx_start</th><th scope=col>days_since_abx_end</th><th scope=col>on_abx</th><th scope=col>rel_abund</th><th scope=col>stool_or_nasal</th><th scope=col>visit_category</th><th scope=col>Age</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>108_V4</td><td>   0</td><td>Other     </td><td>108</td><td>V4</td><td>54</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0000</td><td>Nasal</td><td>Well</td><td>54</td></tr>\n",
       "\t<tr><td>108_V4</td><td>9954</td><td>Bacillales</td><td>108</td><td>V4</td><td>54</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.9954</td><td>Nasal</td><td>Well</td><td>54</td></tr>\n",
       "\t<tr><td>108_V4</td><td>   5</td><td>Other     </td><td>108</td><td>V4</td><td>54</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0005</td><td>Nasal</td><td>Well</td><td>54</td></tr>\n",
       "\t<tr><td>108_V4</td><td>  23</td><td>Other     </td><td>108</td><td>V4</td><td>54</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0023</td><td>Nasal</td><td>Well</td><td>54</td></tr>\n",
       "\t<tr><td>108_V4</td><td>   0</td><td>Other     </td><td>108</td><td>V4</td><td>54</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0000</td><td>Nasal</td><td>Well</td><td>54</td></tr>\n",
       "\t<tr><td>108_V4</td><td>   0</td><td>Other     </td><td>108</td><td>V4</td><td>54</td><td>NA</td><td>NA</td><td>FALSE</td><td>0.0000</td><td>Nasal</td><td>Well</td><td>54</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " Sample & Abundance & Taxonomy & BabyN & VisitCode & age\\_at\\_collection & days\\_since\\_abx\\_start & days\\_since\\_abx\\_end & on\\_abx & rel\\_abund & stool\\_or\\_nasal & visit\\_category & Age\\\\\n",
       " <chr> & <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <lgl> & <dbl> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 108\\_V4 &    0 & Other      & 108 & V4 & 54 & NA & NA & FALSE & 0.0000 & Nasal & Well & 54\\\\\n",
       "\t 108\\_V4 & 9954 & Bacillales & 108 & V4 & 54 & NA & NA & FALSE & 0.9954 & Nasal & Well & 54\\\\\n",
       "\t 108\\_V4 &    5 & Other      & 108 & V4 & 54 & NA & NA & FALSE & 0.0005 & Nasal & Well & 54\\\\\n",
       "\t 108\\_V4 &   23 & Other      & 108 & V4 & 54 & NA & NA & FALSE & 0.0023 & Nasal & Well & 54\\\\\n",
       "\t 108\\_V4 &    0 & Other      & 108 & V4 & 54 & NA & NA & FALSE & 0.0000 & Nasal & Well & 54\\\\\n",
       "\t 108\\_V4 &    0 & Other      & 108 & V4 & 54 & NA & NA & FALSE & 0.0000 & Nasal & Well & 54\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 13\n",
       "\n",
       "| Sample &lt;chr&gt; | Abundance &lt;dbl&gt; | Taxonomy &lt;chr&gt; | BabyN &lt;dbl&gt; | VisitCode &lt;chr&gt; | age_at_collection &lt;dbl&gt; | days_since_abx_start &lt;dbl&gt; | days_since_abx_end &lt;dbl&gt; | on_abx &lt;lgl&gt; | rel_abund &lt;dbl&gt; | stool_or_nasal &lt;chr&gt; | visit_category &lt;chr&gt; | Age &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 108_V4 |    0 | Other      | 108 | V4 | 54 | NA | NA | FALSE | 0.0000 | Nasal | Well | 54 |\n",
       "| 108_V4 | 9954 | Bacillales | 108 | V4 | 54 | NA | NA | FALSE | 0.9954 | Nasal | Well | 54 |\n",
       "| 108_V4 |    5 | Other      | 108 | V4 | 54 | NA | NA | FALSE | 0.0005 | Nasal | Well | 54 |\n",
       "| 108_V4 |   23 | Other      | 108 | V4 | 54 | NA | NA | FALSE | 0.0023 | Nasal | Well | 54 |\n",
       "| 108_V4 |    0 | Other      | 108 | V4 | 54 | NA | NA | FALSE | 0.0000 | Nasal | Well | 54 |\n",
       "| 108_V4 |    0 | Other      | 108 | V4 | 54 | NA | NA | FALSE | 0.0000 | Nasal | Well | 54 |\n",
       "\n"
      ],
      "text/plain": [
       "  Sample Abundance Taxonomy   BabyN VisitCode age_at_collection\n",
       "1 108_V4    0      Other      108   V4        54               \n",
       "2 108_V4 9954      Bacillales 108   V4        54               \n",
       "3 108_V4    5      Other      108   V4        54               \n",
       "4 108_V4   23      Other      108   V4        54               \n",
       "5 108_V4    0      Other      108   V4        54               \n",
       "6 108_V4    0      Other      108   V4        54               \n",
       "  days_since_abx_start days_since_abx_end on_abx rel_abund stool_or_nasal\n",
       "1 NA                   NA                 FALSE  0.0000    Nasal         \n",
       "2 NA                   NA                 FALSE  0.9954    Nasal         \n",
       "3 NA                   NA                 FALSE  0.0005    Nasal         \n",
       "4 NA                   NA                 FALSE  0.0023    Nasal         \n",
       "5 NA                   NA                 FALSE  0.0000    Nasal         \n",
       "6 NA                   NA                 FALSE  0.0000    Nasal         \n",
       "  visit_category Age\n",
       "1 Well           54 \n",
       "2 Well           54 \n",
       "3 Well           54 \n",
       "4 Well           54 \n",
       "5 Well           54 \n",
       "6 Well           54 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "babyN <- 108\n",
    "days_cutoff <- 5\n",
    "\n",
    "taxa_df <- nasal_stool_combined %>% filter(BabyN == babyN)\n",
    "\n",
    "updated_age <- taxa_df %>%\n",
    "    distinct(Sample, stool_or_nasal, age_at_collection) %>%\n",
    "    arrange(age_at_collection, stool_or_nasal) %>%\n",
    "    mutate(age_diff = age_at_collection - lag(age_at_collection),\n",
    "           Age = case_when(\n",
    "               is.na(age_diff) ~ age_at_collection, ## The first entry of diff will always contain one NA value since there is no value before it\n",
    "               Sample == lag(Sample) & stool_or_nasal == \"Stool\" & age_diff <= days_cutoff & lag(stool_or_nasal) == \"Nasal\" ~ lag(age_at_collection), ## Choose nasal age_at_collection (for Stool) if Sample is the same but stool and nasal ages are within age_cutoff of eachother\n",
    "               TRUE ~ age_at_collection\n",
    "               )\n",
    "          ) %>%\n",
    "    select(-age_diff)\n",
    "\n",
    "## Update Age in combined_df\n",
    "taxa_df <- left_join(taxa_df, updated_age)\n",
    "head(taxa_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "80a3e126-4d5c-4c1d-99d5-47eb152ff9b3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Order x-axis by time\n",
    "timepoints <- taxa_df %>%\n",
    "    distinct(Sample, Age) %>%\n",
    "    arrange(Age)\n",
    "\n",
    "taxa_df$Sample <- factor(taxa_df$Sample, levels=timepoints$Sample)\n",
    "\n",
    "taxa_order <- stool_abundance_data %>%\n",
    "    distinct(Raw_Order, Taxonomy) %>%\n",
    "    arrange(Raw_Order) %>%\n",
    "    pull(Taxonomy)\n",
    "\n",
    "taxa_order <- taxa_order[taxa_order %in% unique(taxa_df$Taxonomy)]\n",
    "taxa_order <- c(taxa_order, \"Other\")\n",
    "taxa_df$Taxonomy <- factor(taxa_df$Taxonomy, levels=taxa_order)\n",
    "\n",
    "## Create color palette for taxa barcharts\n",
    "\n",
    "## Use this code if a lot of colors are needed (i.e. >20)\n",
    "# num_taxa <- length( unique(nasal_stool_combined$Taxonomy) )\n",
    "# qual_col_pals <- brewer.pal.info[brewer.pal.info$category == 'qual',]\n",
    "# col_vector <- unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))\n",
    "# set.seed(100)\n",
    "# col_vector <- sample(col_vector[1:num_taxa], num_taxa, replace = FALSE)\n",
    "\n",
    "col_vector <- pal_d3(\"category20\")(length(taxa_order)) ## Grab colors from \"d3\" color palette\n",
    "col_vector[8] <- rgb(1, 1, 0) ## Change grey to yellow\n",
    "col_vector[length(col_vector)] <- rgb(0.5, 0.5, 0.5) ## Assign grey to \"Other\"\n",
    "\n",
    "taxa_barcharts <- ggplot(data=taxa_df, aes(x=factor(Age), y=rel_abund, fill=factor(Taxonomy))) +\n",
    "    geom_col() +\n",
    "    facet_wrap(~stool_or_nasal, ncol=1, scales = \"free_y\") +\n",
    "    theme_classic() +\n",
    "    labs(\n",
    "        y = \"Relative abundance\",\n",
    "        fill = \"Taxonomy\"\n",
    "        ) +\n",
    "    scale_x_discrete(breaks = timepoints$Age, labels = timepoints$Age) +\n",
    "    scale_fill_manual(breaks = taxa_order, values = col_vector) +\n",
    "    theme(legend.justification = 0,\n",
    "          axis.line.x.bottom = element_blank(),\n",
    "          axis.text.x.bottom = element_blank(),\n",
    "          axis.ticks.x.bottom = element_blank(),\n",
    "          axis.title.x.bottom = element_blank(),\n",
    "          legend.background = element_rect(color=\"black\"),\n",
    "          legend.margin = margin(1, 1, 1, 1, \"mm\"),\n",
    "          legend.box.margin = margin(0, 0, 0, 0.5, \"cm\")\n",
    "         ) +\n",
    "    guides(fill = guide_legend(ncol = 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50510485-b4b3-4d6d-acf6-b27655aabbc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "abx_df <- taxa_df %>%\n",
    "    distinct(BabyN, Age, on_abx, days_since_abx_start, days_since_abx_end, age_at_collection) %>%\n",
    "    mutate(on_recent_abx = ifelse(days_since_abx_end <= 7 | on_abx, TRUE, FALSE)) %>%\n",
    "    replace_na(list(on_recent_abx = FALSE))\n",
    "\n",
    "abx_details_by_baby <- abx_details %>% filter(BabyN == paste0(\"Baby\", babyN))\n",
    "\n",
    "\n",
    "if ( nrow(abx_df) > 0 ) {\n",
    "    ## Grab Route from abx_details\n",
    "    abx_df <- abx_df %>%\n",
    "        rowwise() %>%\n",
    "        ## I need to do it this way, rather than using \"on_recent_abx\", because I am indexing abx_details data frame, not abx_df.\n",
    "        mutate(Route = ifelse(\n",
    "            any( between(age_at_collection-abx_details_by_baby$AgeAtStart, 1, 7) | between(age_at_collection-abx_details_by_baby$AgeAtEnd, 0, 7) ),\n",
    "            abx_details_by_baby$Route[which(between(age_at_collection-abx_details_by_baby$AgeAtStart, 1, 7) | between(age_at_collection-abx_details_by_baby$AgeAtEnd, 0, 7))],\n",
    "            NA\n",
    "            )\n",
    "              ) %>%\n",
    "        replace_na(list(Route = \"None\")) %>% ## Needed to replace NA here rather than ifelse statement for some reason\n",
    "        ungroup\n",
    "    abx_df$Route <- factor(abx_df$Route, levels=unique(abx_df$Route))\n",
    "    \n",
    "    on_abx <- ggplot() + geom_point(data=abx_df, aes(x=factor(Age), y=1, fill=factor(Route)), shape=21, size=5) +\n",
    "        theme_void() +\n",
    "        scale_x_discrete(breaks = timepoints$Age, labels = timepoints$Age) +\n",
    "        scale_fill_manual(breaks = c('IM', 'IV', 'oral', 'topical', 'None'),\n",
    "                          labels = c('IM', 'IV', 'Oral', 'Topical', 'None'),\n",
    "                          values = c(\"yellow\", \"blue\", \"red\", \"orange\", \"white\")\n",
    "                         ) +\n",
    "        theme(legend.justification = 0,\n",
    "              legend.background = element_rect(color = \"black\"), ## Add border around legend\n",
    "              legend.margin = margin(1, 1, 1, 1, \"mm\"), ## Add spacing inside legend box\n",
    "              legend.box.margin = margin(0, 0, 0, 0.5, \"cm\") ## Add padding between legend and plot    \n",
    "             ) +\n",
    "        guides(fill = guide_legend(title = \"Antibiotics within 1 week\",\n",
    "                                   title.hjust = 0.5,\n",
    "                                   title.position = \"top\",\n",
    "                                   direction = \"horizontal\"\n",
    "                                  )\n",
    "               )\n",
    "    \n",
    "    } else on_abx <- NULL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd9a6742-9ff5-4a65-8117-0a648a1fab1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Add visit category bar\n",
    "visits_df <- taxa_df %>%\n",
    "    distinct(Age, visit_category)\n",
    "\n",
    "visits <- ggplot() +\n",
    "    geom_point(data=visits_df, aes(x=factor(Age), y=1, fill=visit_category), shape=21, size=5) +\n",
    "    scale_x_discrete(breaks = timepoints$Age, labels = timepoints$Age) +\n",
    "    scale_fill_manual(values = c(\"royalblue\", \"cyan\", \"lightpink\", \"darkgoldenrod\")) +\n",
    "    theme_classic() +\n",
    "    theme(legend.justification = 0,\n",
    "          axis.line.y.left = element_blank(),\n",
    "          axis.text.y.left = element_blank(),\n",
    "          axis.ticks.y.left = element_blank(),\n",
    "          axis.title.y.left = element_blank(),\n",
    "          legend.background = element_rect(color = \"black\"), ## Add border around legend\n",
    "          legend.margin = margin(1, 1, 1, 1, \"mm\"), ## Add spacing inside legend box\n",
    "          legend.box.margin = margin(0, 0, 0, 0.5, \"cm\") ## Add padding between legend and plot  \n",
    "         ) +\n",
    "    labs(x = \"Age (Days)\") +\n",
    "    guides(fill = guide_legend(title = \"Reason for visit\",\n",
    "                               title.hjust = 0,\n",
    "                               title.position = \"top\",\n",
    "                               ncol = 2,\n",
    "                               direction = \"horizontal\"\n",
    "                              )\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2ea9b83-12ca-4498-9c25-115a687e62be",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Plot legends and plots separately\n",
    "\n",
    "## Extract legends from each plot\n",
    "l1 <- get_legend(taxa_barcharts)\n",
    "l2 <- get_legend(on_abx)\n",
    "l3 <- get_legend(visits)\n",
    "\n",
    "## Remove legends from plots\n",
    "p1 <- taxa_barcharts + theme(legend.position = \"none\")\n",
    "p2 <- on_abx + theme(legend.position = \"none\")\n",
    "p3 <- visits + theme(legend.position = \"none\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8879228d-38ec-4a39-9f8e-3e1570a252ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAAPACAYAAAAlpICAAAAEDmlDQ1BrQ0dDb2xvclNwYWNl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\nEAQEAUFAEBAEBAFBQBAQBAQBQSBdIvD/koT8t0JRWrYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=10, repr.plot.height=8)\n",
    "\n",
    "## Arrange legends\n",
    "legends <- plot_grid(l1, l2, l3, rel_heights = c(1, 0.30, 0.30), ncol=1)\n",
    "right_plots <- plot_grid(legends, NULL, ncol = 1, rel_heights = c(1, 0.60)) ## NULL adds an empty plot at the bottom\n",
    "\n",
    "## Arrange plots\n",
    "left_plots <- plot_grid(p1, p2, p3, align = \"v\", ncol=1, axis=\"lr\", rel_heights = c(1.5, 0.05, 0.18))\n",
    "\n",
    "## Combine plots and legends\n",
    "plot <- plot_grid(left_plots, right_plots, rel_widths = c(1, 0.25))\n",
    "plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8042650b-ad84-46c4-b703-6bd23793826f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Save plot\n",
    "ggsave(\"../figures/subject_108_microbiome_over_time.pdf\", plot, height = 8, width = 10, dpi=700)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7efa9a9d-f853-4831-b4a5-fa3538f0d6d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.2.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
